Research on Optimal Allocation Algorithm of Human Resources Based on Data Mining

In the rapidly changing business environment, human resource management (HRM) is facing challenges. Traditional management methods rely on empirical judgment, which leads to insufficient information utilization, low decision-making efficiency and unreasonable resource allocation. As an important technical means in the information age, data mining (DM) technology can extract valuable information from a large number of data, optimize resource allocation and improve the quality of decision-making. This paper discusses the application of DM technology in HRM, and puts forward an intelligent matching algorithm based on cosine similarity, aiming at optimizing HRM configuration. The algorithm integrates multi-source data, constructs personalized talent portraits and job portraits, and uses cosine similarity to intelligently match and recommend, thus realizing efficient and accurate HRM configuration. Empirical research shows that the algorithm can significantly improve the recruitment efficiency, reduce the recruitment cost, and improve the performance of new employees. The research results show that the optimal allocation algorithm of HRM based on DM realizes the efficient and accurate allocation of HR by integrating multi-source data, deeply analyzing features, building personalized talent portraits and intelligent matching and recommendation. The algorithm is not only innovative, but also practical and extensible, which provides new ideas and solutions for enterprise HRM.